pandas.read_csv #

pandas.read_csv(filepath_or_buffer,*,sep=<no_default>,delimiter=None,header='infer',names=<no_default>,index_col=None,usecols=None,dtype=None,engine=None,converters=None,true_values=None,false_values=None,skipinitialspace=False,skiprows=None,skipfooter=0,nrows=None,na_values=None,keep_default_na=True,na_filter=True,verbose=<no_default>,skip_blank_lines=True,parse_dates=None,infer_datetime_format=<no_default>,keep_date_col=<no_default>,date_parser=<no_default>,date_format=None,dayfirst=False,cache_dates=True,iterator=False,chunksize=None,compression='infer',thousands=None,decimal='.',lineterminator=None,quotechar='"',quoting=0,doublequote=True,escapechar=None,comment=None,encoding=None,encoding_errors='strict',dialect=None,on_bad_lines='error',delim_whitespace=<no_default>,low_memory=True,memory_map=False,float_precision=None,storage_options=None,dtype_backend=<no_default>)[source]#

Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the fileinto chunks.

Additional help can be found in the online docs forIO Tools.

Parameters:

filepath_or_bufferstr, path object or file-like object

Any valid string path is acceptable. The string could be a URL. ValidURL schemes include http, ftp, s3, gs, and file. For file URLs, a host isexpected. A local file could be:file://localhost/path/to/table.csv.

If you want to pass in a path object, pandas accepts anyos.PathLike.

By file-like object, we refer to objects with aread() method, such asa file handle (e.g. via builtinopen function) orStringIO.

sepstr, default ‘,’

Character or regex pattern to treat as the delimiter. Ifsep=None, theC engine cannot automatically detectthe separator, but the Python parsing engine can, meaning the latter willbe used and automatically detect the separator from only the first validrow of the file by Python’s builtin sniffer tool,csv.Sniffer.In addition, separators longer than 1 character and different from'\s+' will be interpreted as regular expressions and will also forcethe use of the Python parsing engine. Note that regex delimiters are proneto ignoring quoted data. Regex example:'\r\t'.

delimiterstr, optional

Alias forsep.

headerint, Sequence of int, ‘infer’ or None, default ‘infer’

Row number(s) containing column labels and marking the start of thedata (zero-indexed). Default behavior is to infer the column names: if nonamesare passed the behavior is identical toheader=0 and columnnames are inferred from the first line of the file, if columnnames are passed explicitly tonames then the behavior is identical toheader=None. Explicitly passheader=0 to be able toreplace existing names. The header can be a list of integers thatspecify row locations for aMultiIndex on the columnse.g.[0,1,3]. Intervening rows that are not specified will beskipped (e.g. 2 in this example is skipped). Note that thisparameter ignores commented lines and empty lines ifskip_blank_lines=True, soheader=0 denotes the first line ofdata rather than the first line of the file.

namesSequence of Hashable, optional

Sequence of column labels to apply. If the file contains a header row,then you should explicitly passheader=0 to override the column names.Duplicates in this list are not allowed.

index_colHashable, Sequence of Hashable or False, optional

Column(s) to use as row label(s), denoted either by column labels or columnindices. If a sequence of labels or indices is given,MultiIndexwill be formed for the row labels.

Note:index_col=False can be used to force pandas tonot use the firstcolumn as the index, e.g., when you have a malformed file with delimiters atthe end of each line.

usecolsSequence of Hashable or Callable, optional

Subset of columns to select, denoted either by column labels or column indices.If list-like, all elements must eitherbe positional (i.e. integer indices into the document columns) or stringsthat correspond to column names provided either by the user innames orinferred from the document header row(s). Ifnames are given, the documentheader row(s) are not taken into account. For example, a valid list-likeusecols parameter would be[0,1,2] or['foo','bar','baz'].Element order is ignored, sousecols=[0,1] is the same as[1,0].To instantiate aDataFrame fromdata with element orderpreserved usepd.read_csv(data,usecols=['foo','bar'])[['foo','bar']]for columns in['foo','bar'] order orpd.read_csv(data,usecols=['foo','bar'])[['bar','foo']]for['bar','foo'] order.

If callable, the callable function will be evaluated against the columnnames, returning names where the callable function evaluates toTrue. Anexample of a valid callable argument would belambdax:x.upper()in['AAA','BBB','DDD']. Using this parameter results in much fasterparsing time and lower memory usage.

dtypedtype or dict of {Hashabledtype}, optional

Data type(s) to apply to either the whole dataset or individual columns.E.g.,{'a':np.float64,'b':np.int32,'c':'Int64'}Usestr orobject together with suitablena_values settingsto preserve and not interpretdtype.Ifconverters are specified, they will be applied INSTEADofdtype conversion.

Added in version 1.5.0:Support fordefaultdict was added. Specify adefaultdict as input wherethe default determines thedtype of the columns which are not explicitlylisted.

engine{‘c’, ‘python’, ‘pyarrow’}, optional

Parser engine to use. The C and pyarrow engines are faster, while the python engineis currently more feature-complete. Multithreading is currently only supported bythe pyarrow engine.

Added in version 1.4.0:The ‘pyarrow’ engine was added as anexperimental engine, and some featuresare unsupported, or may not work correctly, with this engine.

convertersdict of {HashableCallable}, optional

Functions for converting values in specified columns. Keys can eitherbe column labels or column indices.

true_valueslist, optional

Values to consider asTrue in addition to case-insensitive variants of ‘True’.

false_valueslist, optional

Values to consider asFalse in addition to case-insensitive variants of ‘False’.

skipinitialspacebool, default False

Skip spaces after delimiter.

skiprowsint, list of int or Callable, optional

Line numbers to skip (0-indexed) or number of lines to skip (int)at the start of the file.

If callable, the callable function will be evaluated against the rowindices, returningTrue if the row should be skipped andFalse otherwise.An example of a valid callable argument would belambdax:xin[0,2].

skipfooterint, default 0

Number of lines at bottom of file to skip (Unsupported withengine='c').

nrowsint, optional

Number of rows of file to read. Useful for reading pieces of large files.

na_valuesHashable, Iterable of Hashable or dict of {HashableIterable}, optional

Additional strings to recognize asNA/NaN. Ifdict passed, specificper-columnNA values. By default the following values are interpreted asNaN: “ “, “#N/A”, “#N/A N/A”, “#NA”, “-1.#IND”, “-1.#QNAN”, “-NaN”, “-nan”,“1.#IND”, “1.#QNAN”, “<NA>”, “N/A”, “NA”, “NULL”, “NaN”, “None”,“n/a”, “nan”, “null “.

keep_default_nabool, default True

Whether or not to include the defaultNaN values when parsing the data.Depending on whetherna_values is passed in, the behavior is as follows:

Ifkeep_default_na isTrue, andna_values are specified,na_valuesis appended to the defaultNaN values used for parsing.
Ifkeep_default_na isTrue, andna_values are not specified, onlythe defaultNaN values are used for parsing.
Ifkeep_default_na isFalse, andna_values are specified, onlytheNaN values specifiedna_values are used for parsing.
Ifkeep_default_na isFalse, andna_values are not specified, nostrings will be parsed asNaN.

Note that ifna_filter is passed in asFalse, thekeep_default_na andna_values parameters will be ignored.

na_filterbool, default True

Detect missing value markers (empty strings and the value ofna_values). Indata without anyNA values, passingna_filter=False can improve theperformance of reading a large file.

verbosebool, default False

Indicate number ofNA values placed in non-numeric columns.

Deprecated since version 2.2.0.

skip_blank_linesbool, default True

IfTrue, skip over blank lines rather than interpreting asNaN values.

parse_datesbool, list of Hashable, list of lists or dict of {Hashablelist}, default False

The behavior is as follows:

bool. IfTrue -> try parsing the index. Note: Automatically set toTrue ifdate_format ordate_parser arguments have been passed.
list ofint or names. e.g. If[1,2,3] -> try parsing columns 1, 2, 3each as a separate date column.
list oflist. e.g. If[[1,3]] -> combine columns 1 and 3 and parseas a single date column. Values are joined with a space before parsing.
dict, e.g.{'foo':[1,3]} -> parse columns 1, 3 as date and callresult ‘foo’. Values are joined with a space before parsing.

If a column or index cannot be represented as an array ofdatetime,say because of an unparsable value or a mixture of timezones, the columnor index will be returned unaltered as anobject data type. Fornon-standarddatetime parsing, useto_datetime() afterread_csv().

Note: A fast-path exists for iso8601-formatted dates.

infer_datetime_formatbool, default False

IfTrue andparse_dates is enabled, pandas will attempt to infer theformat of thedatetime strings in the columns, and if it can be inferred,switch to a faster method of parsing them. In some cases this can increasethe parsing speed by 5-10x.

Deprecated since version 2.0.0:A strict version of this argument is now the default, passing it has no effect.

keep_date_colbool, default False

IfTrue andparse_dates specifies combining multiple columns thenkeep the original columns.

date_parserCallable, optional

Function to use for converting a sequence of string columns to an array ofdatetime instances. The default usesdateutil.parser.parser to do theconversion. pandas will try to calldate_parser in three different ways,advancing to the next if an exception occurs: 1) Pass one or more arrays(as defined byparse_dates) as arguments; 2) concatenate (row-wise) thestring values from the columns defined byparse_dates into a single arrayand pass that; and 3) calldate_parser once for each row using one ormore strings (corresponding to the columns defined byparse_dates) asarguments.

Deprecated since version 2.0.0:Usedate_format instead, or read in asobject and then applyto_datetime() as-needed.

date_formatstr or dict of column -> format, optional

Format to use for parsing dates when used in conjunction withparse_dates.The strftime to parse time, e.g."%d/%m/%Y". Seestrftime documentation for more information on choices, thoughnote that"%f" will parse all the way up to nanoseconds.You can also pass:

“ISO8601”, to parse anyISO8601
time string (not necessarily in exactly the same format);
“mixed”, to infer the format for each element individually. This is risky,
and you should probably use it along withdayfirst.

Added in version 2.0.0.

dayfirstbool, default False

DD/MM format dates, international and European format.

cache_datesbool, default True

IfTrue, use a cache of unique, converted dates to apply thedatetimeconversion. May produce significant speed-up when parsing duplicatedate strings, especially ones with timezone offsets.

iteratorbool, default False

ReturnTextFileReader object for iteration or getting chunks withget_chunk().

chunksizeint, optional

Number of lines to read from the file per chunk. Passing a value will cause thefunction to return aTextFileReader object for iteration.See theIO Tools docsfor more information oniterator andchunksize.

compressionstr or dict, default ‘infer’

For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ ispath-like, then detect compression from the following extensions: ‘.gz’,‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’(otherwise no compression).If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in.Set toNone for no decompression.Can also be a dict with key'method' setto one of {'zip','gzip','bz2','zstd','xz','tar'} andother key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdDecompressor,lzma.LZMAFile ortarfile.TarFile, respectively.As an example, the following could be passed for Zstandard decompression using acustom compression dictionary:compression={'method':'zstd','dict_data':my_compression_dict}.

Added in version 1.5.0:Added support for.tar files.

Changed in version 1.4.0:Zstandard support.

thousandsstr (length 1), optional

Character acting as the thousands separator in numerical values.

decimalstr (length 1), default ‘.’

Character to recognize as decimal point (e.g., use ‘,’ for European data).

lineterminatorstr (length 1), optional

Character used to denote a line break. Only valid with C parser.

quotecharstr (length 1), optional

Character used to denote the start and end of a quoted item. Quoteditems can include thedelimiter and it will be ignored.

quoting{0 or csv.QUOTE_MINIMAL, 1 or csv.QUOTE_ALL, 2 or csv.QUOTE_NONNUMERIC, 3 or csv.QUOTE_NONE}, default csv.QUOTE_MINIMAL

Control field quoting behavior percsv.QUOTE_* constants. Default iscsv.QUOTE_MINIMAL (i.e., 0) which implies that only fields containing specialcharacters are quoted (e.g., characters defined inquotechar,delimiter,orlineterminator.

doublequotebool, default True

Whenquotechar is specified andquoting is notQUOTE_NONE, indicatewhether or not to interpret two consecutivequotechar elements INSIDE afield as a singlequotechar element.

escapecharstr (length 1), optional

Character used to escape other characters.

commentstr (length 1), optional

Character indicating that the remainder of line should not be parsed.If found at the beginningof a line, the line will be ignored altogether. This parameter must be asingle character. Like empty lines (as long asskip_blank_lines=True),fully commented lines are ignored by the parameterheader but not byskiprows. For example, ifcomment='#', parsing#empty\na,b,c\n1,2,3 withheader=0 will result in'a,b,c' beingtreated as the header.

encodingstr, optional, default ‘utf-8’

Encoding to use for UTF when reading/writing (ex.'utf-8').List of Pythonstandard encodings .

encoding_errorsstr, optional, default ‘strict’

How encoding errors are treated.List of possible values .

Added in version 1.3.0.

dialectstr or csv.Dialect, optional

If provided, this parameter will override values (default or not) for thefollowing parameters:delimiter,doublequote,escapechar,skipinitialspace,quotechar, andquoting. If it is necessary tooverride values, aParserWarning will be issued. Seecsv.Dialectdocumentation for more details.

on_bad_lines{‘error’, ‘warn’, ‘skip’} or Callable, default ‘error’

Specifies what to do upon encountering a bad line (a line with too many fields).Allowed values are :

'error', raise an Exception when a bad line is encountered.
'warn', raise a warning when a bad line is encountered and skip that line.
'skip', skip bad lines without raising or warning when they are encountered.

Added in version 1.3.0.

Added in version 1.4.0:

Callable, function with signature(bad_line:list[str])->list[str]|None that will process a singlebad line.bad_line is a list of strings split by thesep.If the function returnsNone, the bad line will be ignored.If the function returns a newlist of strings with more elements thanexpected, aParserWarning will be emitted while dropping extra elements.Only supported whenengine='python'

Changed in version 2.2.0:

Callable, function with signatureas described inpyarrow documentation whenengine='pyarrow'

delim_whitespacebool, default False

Specifies whether or not whitespace (e.g.'' or'\t') will beused as thesep delimiter. Equivalent to settingsep='\s+'. If this optionis set toTrue, nothing should be passed in for thedelimiterparameter.

Deprecated since version 2.2.0:Usesep="\s+" instead.

low_memorybool, default True

Internally process the file in chunks, resulting in lower memory usewhile parsing, but possibly mixed type inference. To ensure no mixedtypes either setFalse, or specify the type with thedtype parameter.Note that the entire file is read into a singleDataFrameregardless, use thechunksize oriterator parameter to return the data inchunks. (Only valid with C parser).

memory_mapbool, default False

If a filepath is provided forfilepath_or_buffer, map the file objectdirectly onto memory and access the data directly from there. Using thisoption can improve performance because there is no longer any I/O overhead.

float_precision{‘high’, ‘legacy’, ‘round_trip’}, optional

Specifies which converter the C engine should use for floating-pointvalues. The options areNone or'high' for the ordinary converter,'legacy' for the original lower precision pandas converter, and'round_trip' for the round-trip converter.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairsare forwarded tourllib.request.Request as header options. For otherURLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs areforwarded tofsspec.open. Please seefsspec andurllib for moredetails, and for more examples on storage options referhere.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultantDataFrame(still experimental). Behaviour is as follows:

"numpy_nullable": returns nullable-dtype-backedDataFrame(default).
"pyarrow": returns pyarrow-backed nullableArrowDtypeDataFrame.

Added in version 2.0.

Returns:

DataFrame or TextFileReader: A comma-separated values (csv) file is returned as two-dimensionaldata structure with labeled axes.

Movatterモバイル変換

pandas.read_csv#

pandas.read_csv #